Re: [GitHub] szha closed pull request #11154: Revert "[MXNET-503] Website landing page for MMS (#11037)"
Hi Sheng, I suggest to put down a reason for such actions later. It may confuse other contributors, e.g., Steffen raised his concern in a private thread. Best Mu > On Jun 10, 2018, at 7:42 PM, Sheng Zha wrote: > > Thanks, Henri. I was reverting the commit on a PR that another committer > didn't intend to merge but only realized afterwards. Given that it wasn't > convenient for him to revert and the negative effect, I committed the > revert and cc'd the original committer in the PR, both as notification and > as a proof of the claim. > > -sz > >> On Sun, Jun 10, 2018 at 10:21 PM, Hen wrote: >> >> It wasn't clear why this was commit was reverted. Things that stood out as >> odd: >> >> * I didn't see an email to dev@ on the topic of a revert. >> * Rather than reverting, if there is a minor item requiring a fix it could >> simply be fixed; if a major item then it should be raised on dev@. >> * I didn't see a reason to revert in the revert PR (11154). >> * The original PR has github:szha asking for github:piiswrong to review >> with no context; I'm concerned that it was implied that the commit could >> not go in without this review. >> * I don't see anything in the original PR to earn a revert. At best >> 'github:john-andrilla' being asked if "a flexible, scalable, >> multi-framework serving solution" was okay. >> * I find it odd that github:lupesko is a reviewer. >> >> Hen >> >> >> >>> On Tue, Jun 5, 2018 at 5:08 PM, GitBox wrote: >>> >>> szha closed pull request #11154: Revert "[MXNET-503] Website landing page >>> for MMS (#11037)" >>> URL: https://github.com/apache/incubator-mxnet/pull/11154 >>> >>> >>> >>> >>> This is a PR merged from a forked repository. >>> As GitHub hides the original diff on merge, it is displayed below for >>> the sake of provenance: >>> >>> As this is a foreign pull request (from a fork), the diff is supplied >>> below (as it won't show otherwise due to GitHub magic): >>> >>> diff --git a/docs/mms/index.md b/docs/mms/index.md >>> deleted file mode 100644 >>> index ff6edae414b..000 >>> --- a/docs/mms/index.md >>> +++ /dev/null >>> @@ -1,114 +0,0 @@ >>> -# Model Server for Apache MXNet (incubating) >>> - >>> -[Model Server for Apache MXNet (incubating)](https://github. >>> com/awslabs/mxnet-model-server), otherwise known as MXNet Model Server >>> (MMS), is an open source project aimed at providing a simple yet scalable >>> solution for model inference. It is a set of command line tools for >>> packaging model archives and serving them. The tools are written in >> Python, >>> and have been extended to support containers for easy deployment and >>> scaling. MMS also supports basic logging and advanced metrics with Amazon >>> CloudWatch integration. >>> - >>> - >>> -## Multi-Framework Model Support with ONNX >>> - >>> -MMS supports both *symbolic* MXNet and *imperative* Gluon models. While >>> the name implies that MMS is just for MXNet, it is in fact much more >>> flexible, as it can support models in the [ONNX](https://onnx.ai) >> format. >>> This means that models created and trained in PyTorch, Caffe2, or other >>> ONNX-supporting frameworks can be served with MMS. >>> - >>> -To find out more about MXNet's support for ONNX models and using ONNX >>> with MMS, refer to the following resources: >>> - >>> -* [MXNet-ONNX Docs](../api/python/contrib/onnx.md) >>> -* [Export an ONNX Model to Serve with MMS](https://github.com/ >>> awslabs/mxnet-model-server/docs/export_from_onnx.md) >>> - >>> -## Getting Started >>> - >>> -To install MMS with ONNX support, make sure you have Python installed, >>> then for Ubuntu run: >>> - >>> -```bash >>> -sudo apt-get install protobuf-compiler libprotoc-dev >>> -pip install mxnet-model-server >>> -``` >>> - >>> -Or for Mac run: >>> - >>> -```bash >>> -conda install -c conda-forge protobuf >>> -pip install mxnet-model-server >>> -``` >>> - >>> - >>> -## Serving a Model >>> - >>> -To serve a model you must first create or download a model archive. >> Visit >>> the [model zoo](https://github.com/awslabs/mxnet-model-server/ >>> docs/model_zoo.md) to browse the models. MMS options can be explored as >>> follows: >>> - >>> -```bash >>> -mxnet-model-server --help >>> -``` >>> - >>> -Here is an easy example for serving an object classification model. You >>> can use any URI and the model will be downloaded first, then served from >>> that location: >>> - >>> -```bash >>> -mxnet-model-server \ >>> - --models squeezenet=https://s3.amazonaws.com/model-server/ >>> models/squeezenet_v1.1/squeezenet_v1.1.model >>> -``` >>> - >>> - >>> -### Test Inference on a Model >>> - >>> -Assuming you have run the previous `mxnet-model-server` command to start >>> serving the object classification model, you can now upload an image to >> its >>> `predict` REST API endpoint. The following will download a picture of a >>> kitten, then upload it to the prediction endpoint. >>> - >>> -```bash >>> -curl -O
Re: [GitHub] szha closed pull request #11154: Revert "[MXNET-503] Website landing page for MMS (#11037)"
Thanks, Henri. I was reverting the commit on a PR that another committer
didn't intend to merge but only realized afterwards. Given that it wasn't
convenient for him to revert and the negative effect, I committed the
revert and cc'd the original committer in the PR, both as notification and
as a proof of the claim.
-sz
On Sun, Jun 10, 2018 at 10:21 PM, Hen wrote:
> It wasn't clear why this was commit was reverted. Things that stood out as
> odd:
>
> * I didn't see an email to dev@ on the topic of a revert.
> * Rather than reverting, if there is a minor item requiring a fix it could
> simply be fixed; if a major item then it should be raised on dev@.
> * I didn't see a reason to revert in the revert PR (11154).
> * The original PR has github:szha asking for github:piiswrong to review
> with no context; I'm concerned that it was implied that the commit could
> not go in without this review.
> * I don't see anything in the original PR to earn a revert. At best
> 'github:john-andrilla' being asked if "a flexible, scalable,
> multi-framework serving solution" was okay.
> * I find it odd that github:lupesko is a reviewer.
>
> Hen
>
>
>
> On Tue, Jun 5, 2018 at 5:08 PM, GitBox wrote:
>
> > szha closed pull request #11154: Revert "[MXNET-503] Website landing page
> > for MMS (#11037)"
> > URL: https://github.com/apache/incubator-mxnet/pull/11154
> >
> >
> >
> >
> > This is a PR merged from a forked repository.
> > As GitHub hides the original diff on merge, it is displayed below for
> > the sake of provenance:
> >
> > As this is a foreign pull request (from a fork), the diff is supplied
> > below (as it won't show otherwise due to GitHub magic):
> >
> > diff --git a/docs/mms/index.md b/docs/mms/index.md
> > deleted file mode 100644
> > index ff6edae414b..000
> > --- a/docs/mms/index.md
> > +++ /dev/null
> > @@ -1,114 +0,0 @@
> > -# Model Server for Apache MXNet (incubating)
> > -
> > -[Model Server for Apache MXNet (incubating)](https://github.
> > com/awslabs/mxnet-model-server), otherwise known as MXNet Model Server
> > (MMS), is an open source project aimed at providing a simple yet scalable
> > solution for model inference. It is a set of command line tools for
> > packaging model archives and serving them. The tools are written in
> Python,
> > and have been extended to support containers for easy deployment and
> > scaling. MMS also supports basic logging and advanced metrics with Amazon
> > CloudWatch integration.
> > -
> > -
> > -## Multi-Framework Model Support with ONNX
> > -
> > -MMS supports both *symbolic* MXNet and *imperative* Gluon models. While
> > the name implies that MMS is just for MXNet, it is in fact much more
> > flexible, as it can support models in the [ONNX](https://onnx.ai)
> format.
> > This means that models created and trained in PyTorch, Caffe2, or other
> > ONNX-supporting frameworks can be served with MMS.
> > -
> > -To find out more about MXNet's support for ONNX models and using ONNX
> > with MMS, refer to the following resources:
> > -
> > -* [MXNet-ONNX Docs](../api/python/contrib/onnx.md)
> > -* [Export an ONNX Model to Serve with MMS](https://github.com/
> > awslabs/mxnet-model-server/docs/export_from_onnx.md)
> > -
> > -## Getting Started
> > -
> > -To install MMS with ONNX support, make sure you have Python installed,
> > then for Ubuntu run:
> > -
> > -```bash
> > -sudo apt-get install protobuf-compiler libprotoc-dev
> > -pip install mxnet-model-server
> > -```
> > -
> > -Or for Mac run:
> > -
> > -```bash
> > -conda install -c conda-forge protobuf
> > -pip install mxnet-model-server
> > -```
> > -
> > -
> > -## Serving a Model
> > -
> > -To serve a model you must first create or download a model archive.
> Visit
> > the [model zoo](https://github.com/awslabs/mxnet-model-server/
> > docs/model_zoo.md) to browse the models. MMS options can be explored as
> > follows:
> > -
> > -```bash
> > -mxnet-model-server --help
> > -```
> > -
> > -Here is an easy example for serving an object classification model. You
> > can use any URI and the model will be downloaded first, then served from
> > that location:
> > -
> > -```bash
> > -mxnet-model-server \
> > - --models squeezenet=https://s3.amazonaws.com/model-server/
> > models/squeezenet_v1.1/squeezenet_v1.1.model
> > -```
> > -
> > -
> > -### Test Inference on a Model
> > -
> > -Assuming you have run the previous `mxnet-model-server` command to start
> > serving the object classification model, you can now upload an image to
> its
> > `predict` REST API endpoint. The following will download a picture of a
> > kitten, then upload it to the prediction endpoint.
> > -
> > -```bash
> > -curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg
> > -curl -X POST http://127.0.0.1:8080/squeezenet/predict -F
> > "data=@kitten.jpg"
> > -```
> > -
> > -The predict endpoint will return a prediction response in JSON. It will
> > look something like the following result:
> > -
> > -```
> > -{
> > - "prediction": [
> > -
Re: [GitHub] szha closed pull request #11154: Revert "[MXNET-503] Website landing page for MMS (#11037)"
It wasn't clear why this was commit was reverted. Things that stood out as
odd:
* I didn't see an email to dev@ on the topic of a revert.
* Rather than reverting, if there is a minor item requiring a fix it could
simply be fixed; if a major item then it should be raised on dev@.
* I didn't see a reason to revert in the revert PR (11154).
* The original PR has github:szha asking for github:piiswrong to review
with no context; I'm concerned that it was implied that the commit could
not go in without this review.
* I don't see anything in the original PR to earn a revert. At best
'github:john-andrilla' being asked if "a flexible, scalable,
multi-framework serving solution" was okay.
* I find it odd that github:lupesko is a reviewer.
Hen
On Tue, Jun 5, 2018 at 5:08 PM, GitBox wrote:
> szha closed pull request #11154: Revert "[MXNET-503] Website landing page
> for MMS (#11037)"
> URL: https://github.com/apache/incubator-mxnet/pull/11154
>
>
>
>
> This is a PR merged from a forked repository.
> As GitHub hides the original diff on merge, it is displayed below for
> the sake of provenance:
>
> As this is a foreign pull request (from a fork), the diff is supplied
> below (as it won't show otherwise due to GitHub magic):
>
> diff --git a/docs/mms/index.md b/docs/mms/index.md
> deleted file mode 100644
> index ff6edae414b..000
> --- a/docs/mms/index.md
> +++ /dev/null
> @@ -1,114 +0,0 @@
> -# Model Server for Apache MXNet (incubating)
> -
> -[Model Server for Apache MXNet (incubating)](https://github.
> com/awslabs/mxnet-model-server), otherwise known as MXNet Model Server
> (MMS), is an open source project aimed at providing a simple yet scalable
> solution for model inference. It is a set of command line tools for
> packaging model archives and serving them. The tools are written in Python,
> and have been extended to support containers for easy deployment and
> scaling. MMS also supports basic logging and advanced metrics with Amazon
> CloudWatch integration.
> -
> -
> -## Multi-Framework Model Support with ONNX
> -
> -MMS supports both *symbolic* MXNet and *imperative* Gluon models. While
> the name implies that MMS is just for MXNet, it is in fact much more
> flexible, as it can support models in the [ONNX](https://onnx.ai) format.
> This means that models created and trained in PyTorch, Caffe2, or other
> ONNX-supporting frameworks can be served with MMS.
> -
> -To find out more about MXNet's support for ONNX models and using ONNX
> with MMS, refer to the following resources:
> -
> -* [MXNet-ONNX Docs](../api/python/contrib/onnx.md)
> -* [Export an ONNX Model to Serve with MMS](https://github.com/
> awslabs/mxnet-model-server/docs/export_from_onnx.md)
> -
> -## Getting Started
> -
> -To install MMS with ONNX support, make sure you have Python installed,
> then for Ubuntu run:
> -
> -```bash
> -sudo apt-get install protobuf-compiler libprotoc-dev
> -pip install mxnet-model-server
> -```
> -
> -Or for Mac run:
> -
> -```bash
> -conda install -c conda-forge protobuf
> -pip install mxnet-model-server
> -```
> -
> -
> -## Serving a Model
> -
> -To serve a model you must first create or download a model archive. Visit
> the [model zoo](https://github.com/awslabs/mxnet-model-server/
> docs/model_zoo.md) to browse the models. MMS options can be explored as
> follows:
> -
> -```bash
> -mxnet-model-server --help
> -```
> -
> -Here is an easy example for serving an object classification model. You
> can use any URI and the model will be downloaded first, then served from
> that location:
> -
> -```bash
> -mxnet-model-server \
> - --models squeezenet=https://s3.amazonaws.com/model-server/
> models/squeezenet_v1.1/squeezenet_v1.1.model
> -```
> -
> -
> -### Test Inference on a Model
> -
> -Assuming you have run the previous `mxnet-model-server` command to start
> serving the object classification model, you can now upload an image to its
> `predict` REST API endpoint. The following will download a picture of a
> kitten, then upload it to the prediction endpoint.
> -
> -```bash
> -curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg
> -curl -X POST http://127.0.0.1:8080/squeezenet/predict -F
> "data=@kitten.jpg"
> -```
> -
> -The predict endpoint will return a prediction response in JSON. It will
> look something like the following result:
> -
> -```
> -{
> - "prediction": [
> -[
> - {
> -"class": "n02124075 Egyptian cat",
> -"probability": 0.9408261179924011
> - },
> -...
> -```
> -
> -For more examples of serving models visit the following resources:
> -
> -* [Quickstart: Model Serving](https://github.com/
> awslabs/mxnet-model-server/README.md#serve-a-model)
> -* [Running the Model Server](https://github.com/
> awslabs/mxnet-model-server/docs/server.md)
> -
> -
> -## Create a Model Archive
> -
> -Creating a model archive involves rounding up the required model
> artifacts, then using the `mxnet-model-export` command line interface. The
> process for creating
